Solver(求解器) 模块

ppsci.solver

Solver

Class for solver.

Parameters:

Name	Type	Description	Default
model	Layer	Model.	required
constraint	Optional[Dict[str, Constraint]]	Constraint(s) applied on model. Defaults to None.	None
output_dir	Optional[str]	Output directory. Defaults to "./output/".	'./output/'
optimizer	Optional[Optimizer]	Optimizer object. Defaults to None.	None
lr_scheduler	Optional[LRScheduler]	Learning rate scheduler. Defaults to None.	None
epochs	int	Training epoch(s). Defaults to 5.	5
iters_per_epoch	int	Number of iterations within an epoch. Defaults to 20.	20
update_freq	int	Update frequency of parameters. Defaults to 1.	1
save_freq	int	Saving frequency for checkpoint. Defaults to 0.	0
log_freq	int	Logging frequency. Defaults to 10.	10
eval_during_train	bool	Whether evaluate model during training. Defaults to False.	False
start_eval_epoch	int	Epoch number evaluation applied begin after. Defaults to 1.	1
eval_freq	int	Evaluation frequency. Defaults to 1.	1
seed	int	Random seed. Defaults to 42.	42
use_vdl	Optional[bool]	Whether use VisualDL to log scalars. Defaults to False.	False
use_wandb	Optional[bool]	Whether use wandb to log data. Defaults to False.	False
use_tbd	Optional[bool]	Whether use tensorboardX to log data. Defaults to False.	False
wandb_config	Optional[Dict[str, str]]	Config dict of WandB. Defaults to None.	None
device	Literal['cpu', 'gpu', 'xpu']	Runtime device. Defaults to "gpu".	'gpu'
equation	Optional[Dict[str, PDE]]	Equation dict. Defaults to None.	None
geom	Optional[Dict[str, Geometry]]	Geometry dict. Defaults to None.	None
validator	Optional[Dict[str, Validator]]	Validator dict. Defaults to None.	None
visualizer	Optional[Dict[str, Visualizer]]	Visualizer dict. Defaults to None.	None
use_amp	bool	Whether use AMP. Defaults to False.	False
amp_level	Literal['O0', 'O1', 'O2', 'OD']	AMP level. Defaults to "O1".	'O1'
pretrained_model_path	Optional[str]	Pretrained model path. Defaults to None.	None
checkpoint_path	Optional[str]	Checkpoint path. Defaults to None.	None
compute_metric_by_batch	bool	Whether calculate metrics after each batch during evaluation. Defaults to False.	False
eval_with_no_grad	bool	Whether set stop_gradient=True for every Tensor if no differentiation involved during computation, generally for save GPU memory and accelerate computing. Defaults to False.	False
to_static	bool	Whether enable to_static for forward pass. Defaults to False.	False
loss_aggregator	Optional[LossAggregator]	Loss aggregator, such as a multi-task learning loss aggregator. Defaults to None.	None
cfg	Optional[DictConfig]	(Optional[DictConfig]): Running config dict. Defaults to None. NOTE: This will be required in the future.	None

Examples:

>>> import ppsci
>>> model = ppsci.arch.MLP(("x",), ("u",), 5, 20)
>>> opt = ppsci.optimizer.AdamW(1e-3)(model)
>>> geom = ppsci.geometry.Rectangle((0, 0), (1, 1))
>>> pde_constraint = ppsci.constraint.InteriorConstraint(
...     {"u": lambda out: out["u"]},
...     {"u": 0},
...     geom,
...     {
...         "dataset": "IterableNamedArrayDataset",
...         "iters_per_epoch": 1,
...         "batch_size": 16,
...     },
...     ppsci.loss.MSELoss("mean"),
...     name="EQ",
... )
>>> solver = ppsci.solver.Solver(
...     model,
...     {"EQ": pde_constraint},
...     "./output",
...     opt,
...     None,
... )

Source code in ppsci/solver/solver.py

class Solver:
    """Class for solver.

    Args:
        model (nn.Layer): Model.
        constraint (Optional[Dict[str, ppsci.constraint.Constraint]]): Constraint(s) applied on model. Defaults to None.
        output_dir (Optional[str]): Output directory. Defaults to "./output/".
        optimizer (Optional[optimizer.Optimizer]): Optimizer object. Defaults to None.
        lr_scheduler (Optional[optimizer.lr.LRScheduler]): Learning rate scheduler. Defaults to None.
        epochs (int, optional): Training epoch(s). Defaults to 5.
        iters_per_epoch (int, optional): Number of iterations within an epoch. Defaults to 20.
        update_freq (int, optional): Update frequency of parameters. Defaults to 1.
        save_freq (int, optional): Saving frequency for checkpoint. Defaults to 0.
        log_freq (int, optional): Logging frequency. Defaults to 10.
        eval_during_train (bool, optional): Whether evaluate model during training. Defaults to False.
        start_eval_epoch (int, optional): Epoch number evaluation applied begin after. Defaults to 1.
        eval_freq (int, optional): Evaluation frequency. Defaults to 1.
        seed (int, optional): Random seed. Defaults to 42.
        use_vdl (Optional[bool]): Whether use VisualDL to log scalars. Defaults to False.
        use_wandb (Optional[bool]): Whether use wandb to log data. Defaults to False.
        use_tbd (Optional[bool]): Whether use tensorboardX to log data. Defaults to False.
        wandb_config (Optional[Dict[str, str]]): Config dict of WandB. Defaults to None.
        device (Literal["cpu", "gpu", "xpu"], optional): Runtime device. Defaults to "gpu".
        equation (Optional[Dict[str, ppsci.equation.PDE]]): Equation dict. Defaults to None.
        geom (Optional[Dict[str, ppsci.geometry.Geometry]]): Geometry dict. Defaults to None.
        validator (Optional[Dict[str, ppsci.validate.Validator]]): Validator dict. Defaults to None.
        visualizer (Optional[Dict[str, ppsci.visualize.Visualizer]]): Visualizer dict. Defaults to None.
        use_amp (bool, optional): Whether use AMP. Defaults to False.
        amp_level (Literal["O0", "O1", "O2", "OD"], optional): AMP level. Defaults to "O1".
        pretrained_model_path (Optional[str]): Pretrained model path. Defaults to None.
        checkpoint_path (Optional[str]): Checkpoint path. Defaults to None.
        compute_metric_by_batch (bool, optional): Whether calculate metrics after each batch during evaluation. Defaults to False.
        eval_with_no_grad (bool, optional): Whether set `stop_gradient=True` for every Tensor if no differentiation
            involved during computation, generally for save GPU memory and accelerate computing. Defaults to False.
        to_static (bool, optional): Whether enable to_static for forward pass. Defaults to False.
        loss_aggregator (Optional[mtl.LossAggregator]): Loss aggregator, such as a multi-task learning loss aggregator. Defaults to None.
        cfg: (Optional[DictConfig]): Running config dict. Defaults to None. NOTE: This will be required in the future.

    Examples:
        >>> import ppsci
        >>> model = ppsci.arch.MLP(("x",), ("u",), 5, 20)
        >>> opt = ppsci.optimizer.AdamW(1e-3)(model)
        >>> geom = ppsci.geometry.Rectangle((0, 0), (1, 1))
        >>> pde_constraint = ppsci.constraint.InteriorConstraint(
        ...     {"u": lambda out: out["u"]},
        ...     {"u": 0},
        ...     geom,
        ...     {
        ...         "dataset": "IterableNamedArrayDataset",
        ...         "iters_per_epoch": 1,
        ...         "batch_size": 16,
        ...     },
        ...     ppsci.loss.MSELoss("mean"),
        ...     name="EQ",
        ... )  # doctest: +SKIP
        >>> solver = ppsci.solver.Solver(
        ...     model,
        ...     {"EQ": pde_constraint},
        ...     "./output",
        ...     opt,
        ...     None,
        ... )  # doctest: +SKIP
    """

    def __init__(
        self,
        model: nn.Layer,
        constraint: Optional[Dict[str, ppsci.constraint.Constraint]] = None,
        output_dir: Optional[str] = "./output/",
        optimizer: Optional[optim.Optimizer] = None,
        lr_scheduler: Optional[optim.lr.LRScheduler] = None,
        epochs: int = 5,
        iters_per_epoch: int = 20,
        update_freq: int = 1,
        save_freq: int = 0,
        log_freq: int = 10,
        eval_during_train: bool = False,
        start_eval_epoch: int = 1,
        eval_freq: int = 1,
        seed: int = 42,
        use_vdl: bool = False,
        use_wandb: bool = False,
        use_tbd: bool = False,
        wandb_config: Optional[Mapping] = None,
        device: Literal["cpu", "gpu", "xpu"] = "gpu",
        equation: Optional[Dict[str, ppsci.equation.PDE]] = None,
        geom: Optional[Dict[str, ppsci.geometry.Geometry]] = None,
        validator: Optional[Dict[str, ppsci.validate.Validator]] = None,
        visualizer: Optional[Dict[str, ppsci.visualize.Visualizer]] = None,
        use_amp: bool = False,
        amp_level: Literal["O0", "O1", "O2", "OD"] = "O1",
        pretrained_model_path: Optional[str] = None,
        checkpoint_path: Optional[str] = None,
        compute_metric_by_batch: bool = False,
        eval_with_no_grad: bool = False,
        to_static: bool = False,
        loss_aggregator: Optional[mtl.LossAggregator] = None,
        *,
        cfg: Optional[DictConfig] = None,
    ):
        self.cfg = cfg
        if isinstance(cfg, DictConfig):
            # (Recommended)Params can be passed within cfg
            # rather than passed to 'Solver.__init__' one-by-one.
            self._parse_params_from_cfg(cfg)

        # set model
        self.model = model
        # set constraint
        self.constraint = constraint
        # set output directory
        if not cfg:
            self.output_dir = output_dir

        # set optimizer
        self.optimizer = optimizer
        # set learning rate scheduler
        if lr_scheduler is not None:
            logger.warning(
                "The argument: 'lr_scheduler' now automatically retrieves from "
                "'optimizer._learning_rate' when 'optimizer' is given, so it is "
                "recommended to remove it from the Solver's initialization arguments."
            )
        self.lr_scheduler = (
            optimizer._learning_rate
            if (
                isinstance(optimizer, optim.Optimizer)
                and isinstance(optimizer._learning_rate, optim.lr.LRScheduler)
            )
            else None
        )
        if isinstance(self.optimizer, ppsci.optimizer.OptimizerList):
            self.lr_scheduler = ppsci.optimizer.lr_scheduler.SchedulerList(
                tuple(
                    opt._learning_rate
                    for opt in self.optimizer
                    if isinstance(opt._learning_rate, optim.lr.LRScheduler)
                )
            )

        # set training hyper-parameter
        if not cfg:
            self.epochs = epochs
            self.iters_per_epoch = iters_per_epoch
            # set update_freq for gradient accumulation
            self.update_freq = update_freq
            # set checkpoint saving frequency
            self.save_freq = save_freq
            # set logging frequency
            self.log_freq = log_freq

            # set evaluation hyper-parameter
            self.eval_during_train = eval_during_train
            self.start_eval_epoch = start_eval_epoch
            self.eval_freq = eval_freq

        # initialize training log(training loss, time cost, etc.) recorder during one epoch
        self.train_output_info: Dict[str, misc.AverageMeter] = {}
        self.train_time_info = {
            "batch_cost": misc.AverageMeter("batch_cost", ".5f", postfix="s"),
            "reader_cost": misc.AverageMeter("reader_cost", ".5f", postfix="s"),
        }
        self.train_loss_info: Dict[str, misc.AverageMeter] = {}

        # initialize evaluation log(evaluation loss, metric, etc.) recorder.
        self.eval_output_info: Dict[str, misc.AverageMeter] = {}
        self.eval_time_info = {
            "batch_cost": misc.AverageMeter("batch_cost", ".5f", postfix="s"),
            "reader_cost": misc.AverageMeter("reader_cost", ".5f", postfix="s"),
        }

        # set running device
        if not cfg:
            self.device = device
        if self.device != "cpu" and paddle.device.get_device() == "cpu":
            logger.warning(f"Set device({device}) to 'cpu' for only cpu available.")
            self.device = "cpu"
        self.device = paddle.set_device(self.device)

        # set equations for physics-driven or data-physics hybrid driven task, such as PINN
        self.equation = equation

        # set validator
        self.validator = validator

        # set visualizer
        self.visualizer = visualizer

        # set automatic mixed precision(AMP) configuration
        if not cfg:
            self.use_amp = use_amp
            self.amp_level = amp_level
        self.scaler = amp.GradScaler(True) if self.use_amp else None

        # whether calculate metrics by each batch during evaluation, mainly for memory efficiency
        if not cfg:
            self.compute_metric_by_batch = compute_metric_by_batch
        if validator is not None:
            for metric in itertools.chain(
                *[_v.metric.values() for _v in self.validator.values()]
            ):
                if metric.keep_batch ^ self.compute_metric_by_batch:
                    raise ValueError(
                        f"{misc.typename(metric)}.keep_batch should be "
                        f"{self.compute_metric_by_batch} when compute_metric_by_batch="
                        f"{self.compute_metric_by_batch}."
                    )
        # whether set `stop_gradient=True` for every Tensor if no differentiation involved during evaluation
        if not cfg:
            self.eval_with_no_grad = eval_with_no_grad

        self.rank = dist.get_rank()
        self.world_size = dist.get_world_size()
        # initialize distributed environment
        if self.world_size > 1:
            # TODO(sensen): support different kind of DistributedStrategy
            fleet.init(is_collective=True)
            logger.warning(
                f"Detected 'world_size'({self.world_size}) > 1, it is recommended to "
                "scale up the learning rate and reduce the 'epochs' or "
                "'iters_per_epoch' according to the 'world_size' both linearly if you "
                "are training model."
            )

        # set moving average model(optional)
        self.ema_model = None
        if self.cfg and any(key in self.cfg.TRAIN for key in ["ema", "swa"]):
            if "ema" in self.cfg.TRAIN and cfg.TRAIN.ema.get("use_ema", False):
                self.ema_model = ema.ExponentialMovingAverage(
                    self.model, self.cfg.TRAIN.ema.decay
                )
            elif "swa" in self.cfg.TRAIN and cfg.TRAIN.swa.get("use_swa", False):
                self.ema_model = ema.StochasticWeightAverage(self.model)

        # load pretrained model, usually used for transfer learning
        if not cfg:
            self.pretrained_model_path = pretrained_model_path
        if self.pretrained_model_path is not None:
            save_load.load_pretrain(
                self.model, self.pretrained_model_path, self.equation
            )

        # initialize an dict for tracking best metric during training
        self.best_metric = {
            "metric": float("inf"),
            "epoch": 0,
        }
        # load model checkpoint, usually used for resume training
        if not cfg:
            self.checkpoint_path = checkpoint_path
        if self.checkpoint_path is not None:
            if self.pretrained_model_path is not None:
                logger.warning(
                    "Detected 'pretrained_model_path' is given, weights in which might be"
                    "overridden by weights loaded from given 'checkpoint_path'."
                )
            loaded_metric = save_load.load_checkpoint(
                self.checkpoint_path,
                self.model,
                self.optimizer,
                self.scaler,
                self.equation,
                self.ema_model,
            )
            if isinstance(loaded_metric, dict):
                self.best_metric.update(loaded_metric)

        # decorate model(s) and optimizer(s) for AMP
        if self.use_amp:
            self.model, self.optimizer = amp.decorate(
                self.model,
                self.optimizer,
                self.amp_level,
                save_dtype="float32",
            )

        # choosing an appropriate training function for different optimizers
        if misc.typename(self.optimizer) == "LBFGS":
            if self.use_amp:
                raise ValueError(
                    "Auto Mix Precision is not supported for L-BFGS optimizer."
                )
            self.train_epoch_func = ppsci.solver.train.train_LBFGS_epoch_func
            if self.update_freq != 1:
                self.update_freq = 1
                logger.warning("Set 'update_freq' to to 1 when using L-BFGS optimizer.")
        else:
            self.train_epoch_func = ppsci.solver.train.train_epoch_func

        # wrap model and optimizer to parallel object
        if self.world_size > 1:
            if isinstance(self.model, paddle.DataParallel):
                raise ValueError(
                    "Given model is already wrapped by paddle.DataParallel."
                    "Please do not wrap your model with DataParallel "
                    "before 'Solver.__init__' and keep it's type as 'nn.Layer'."
                )

            def dist_wrapper(model: nn.Layer) -> paddle.DataParallel:
                dist_model = fleet.distributed_model(model)
                if hasattr(model, "input_keys"):
                    dist_model.input_keys = dist_model._layers.input_keys
                if hasattr(model, "output_keys"):
                    dist_model.output_keys = dist_model._layers.output_keys
                return dist_model

            if isinstance(self.model, ppsci.arch.ModelList):
                for i in range(len(self.model.model_list)):
                    # NOTE: Convert each model in model_list to DataParallel
                    self.model.model_list[i] = dist_wrapper(self.model.model_list[i])
            else:
                self.model = dist_wrapper(self.model)

            if self.optimizer is not None:
                self.optimizer = fleet.distributed_optimizer(self.optimizer)

        # set VisualDL tool
        self.vdl_writer = None
        if not cfg:
            self.use_vdl = use_vdl
        if self.use_vdl:
            with misc.RankZeroOnly(self.rank) as is_master:
                if is_master:
                    self.vdl_writer = vdl.LogWriter(osp.join(output_dir, "vdl"))
            logger.info(
                "VisualDL is enabled for logging, you can view it by "
                f"running:\nvisualdl --logdir {self.vdl_writer._logdir} --port 8080"
            )

        # set WandB tool
        self.wandb_writer = None
        if not cfg:
            self.use_wandb = use_wandb
        if self.use_wandb:
            try:
                import wandb
            except ModuleNotFoundError:
                raise ModuleNotFoundError(
                    "Please install 'wandb' with `pip install wandb` first."
                )
            with misc.RankZeroOnly(self.rank) as is_master:
                if is_master:
                    self.wandb_writer = wandb.init(**wandb_config)

        # set TensorBoardX tool
        self.tbd_writer = None
        if not cfg:
            self.use_tbd = use_tbd
        if self.use_tbd:
            try:
                import tensorboardX
            except ModuleNotFoundError:
                raise ModuleNotFoundError(
                    "Please install 'tensorboardX' with `pip install tensorboardX` first."
                )
            with misc.RankZeroOnly(self.rank) as is_master:
                if is_master:
                    self.tbd_writer = tensorboardX.SummaryWriter(
                        osp.join(output_dir, "tensorboard")
                    )
            logger.message(
                "TensorboardX is enabled for logging, you can view it by "
                f"running:\ntensorboard --logdir {self.tbd_writer.logdir}"
            )

        self.global_step = 0

        # log paddlepaddle's version
        if version.Version(paddle.__version__) != version.Version("0.0.0"):
            paddle_version = paddle.__version__
            if version.Version(paddle.__version__) < version.Version("2.6.0"):
                logger.warning(
                    f"Detected paddlepaddle version is '{paddle_version}', "
                    "currently it is recommended to use release 2.6 or develop version."
                )
        else:
            paddle_version = f"develop({paddle.version.commit[:7]})"

        logger.info(f"Using paddlepaddle {paddle_version} on device {self.device}")

        self.forward_helper = expression.ExpressionSolver()

        # whether enable static for forward pass, defaults to False
        jit.enable_to_static(to_static)
        logger.info(f"Set to_static={to_static} for computational optimization.")

        # use loss aggregator, use Sum if None
        if isinstance(loss_aggregator, (mtl.AGDA, mtl.PCGrad)) and self.use_amp:
            raise ValueError(
                "Auto Mix Precision do not support AGDA, PCGrad loss aggregator yet, "
                "please set use_amp=False."
            )
        self.loss_aggregator = loss_aggregator or mtl.Sum()

        # convert sympy to callable object if exist
        extra_parameters = []
        if self.equation:
            for equation in self.equation.values():
                extra_parameters += list(equation.learnable_parameters)

        def convert_expr(
            container_dict: Union[
                Dict[str, ppsci.constraint.Constraint],
                Dict[str, ppsci.validate.Validator],
                Dict[str, ppsci.visualize.Visualizer],
            ]
        ) -> None:
            for container in container_dict.values():
                exprs = [
                    expr
                    for expr in container.output_expr.values()
                    if isinstance(expr, sp.Basic)
                ]
                if len(exprs) > 0:
                    funcs = ppsci.lambdify(
                        exprs,
                        self.model,
                        extra_parameters=extra_parameters,
                        # graph_filename=osp.join(self.output_dir, "symbolic_graph_visual"),  # HACK: Activate it for DEBUG.
                        fuse_derivative=True,
                    )
                    ind = 0
                    for name in container.output_expr:
                        if isinstance(container.output_expr[name], sp.Basic):
                            container.output_expr[name] = funcs[ind]
                            if self.world_size > 1:
                                container.output_expr[name] = dist_wrapper(
                                    container.output_expr[name]
                                )
                            ind += 1

        if self.constraint:
            convert_expr(self.constraint)

        if self.validator:
            convert_expr(self.validator)

        if self.visualizer:
            convert_expr(self.visualizer)

        # set up benchmark flag, will print memory stat if enabled
        self.benchmark_flag: bool = os.getenv("BENCHMARK_ROOT", None) is not None

        # set up nvtx flag for nsight analysis
        self.nvtx_flag: bool = os.getenv("NVTX", None) is not None
        self.forward_helper.nvtx_flag = self.nvtx_flag

    def train(self) -> None:
        """Training."""
        self.global_step = self.best_metric["epoch"] * self.iters_per_epoch
        start_epoch = self.best_metric["epoch"] + 1

        if self.nvtx_flag:
            core.nvprof_start()
            core.nvprof_enable_record_event()

        for epoch_id in range(start_epoch, self.epochs + 1):
            self.train_epoch_func(self, epoch_id, self.log_freq)
            self.train_output_info.clear()

            # update average model if exist
            if self.ema_model and epoch_id % self.avg_freq == 0:
                self.ema_model.update()

            cur_metric = float("inf")
            # evaluate during training
            if (
                self.eval_during_train
                and epoch_id % self.eval_freq == 0
                and epoch_id >= self.start_eval_epoch
            ):
                cur_metric, metric_dict_group = self.eval(epoch_id)
                if cur_metric < self.best_metric["metric"]:
                    self.best_metric["metric"] = cur_metric
                    self.best_metric["epoch"] = epoch_id
                    save_load.save_checkpoint(
                        self.model,
                        self.optimizer,
                        self.best_metric,
                        self.scaler,
                        self.output_dir,
                        "best_model",
                        self.equation,
                    )
                logger.info(
                    f"[Eval][Epoch {epoch_id}]"
                    f"[best metric: {self.best_metric['metric']}]"
                )
                for metric_name, metric_dict in metric_dict_group.items():
                    logger.scalar(
                        {f"eval/{metric_name}/{k}": v for k, v in metric_dict.items()},
                        epoch_id,
                        self.vdl_writer,
                        self.wandb_writer,
                        self.tbd_writer,
                    )

                # visualize after evaluation
                if self.visualizer is not None:
                    self.visualize(epoch_id)

                # evaluation for moving average evaluation(almost same procedure)
                if self.ema_model and epoch_id % self.avg_freq == 0:
                    self.ema_model.apply_shadow()
                    logger.info("Evaluating metric of averaging model...")
                    cur_metric_ema, metric_dict_group_ema = self.eval(epoch_id)
                    self.ema_model.restore()

                    if cur_metric_ema < self.best_metric["metric"]:
                        self.best_metric["metric"] = cur_metric_ema
                        self.best_metric["epoch"] = epoch_id
                        save_load.save_checkpoint(
                            self.ema_model,
                            None,
                            metric=self.best_metric,
                            output_dir=self.output_dir,
                            prefix="best_model_ema",
                        )
                    logger.info(
                        f"[Eval][Epoch {epoch_id}]"
                        f"[best metric: {self.best_metric['metric']}]"
                    )
                    for metric_name, metric_dict in metric_dict_group_ema.items():
                        logger.scalar(
                            {
                                f"eval_ema/{metric_name}/{k}": v
                                for k, v in metric_dict.items()
                            },
                            epoch_id,
                            self.vdl_writer,
                            self.wandb_writer,
                            self.tbd_writer,
                        )

            # update learning rate by epoch
            if self.lr_scheduler is not None and self.lr_scheduler.by_epoch:
                self.lr_scheduler.step()

            # save epoch model every save_freq epochs
            if self.save_freq > 0 and epoch_id % self.save_freq == 0:
                save_load.save_checkpoint(
                    self.model,
                    self.optimizer,
                    {"metric": cur_metric, "epoch": epoch_id},
                    self.scaler,
                    self.output_dir,
                    f"epoch_{epoch_id}",
                    self.equation,
                    ema_model=self.ema_model,
                )

            # save the latest model for convenient resume training
            save_load.save_checkpoint(
                self.model,
                self.optimizer,
                {"metric": cur_metric, "epoch": epoch_id},
                self.scaler,
                self.output_dir,
                "latest",
                self.equation,
                print_log=(epoch_id == start_epoch),
                ema_model=self.ema_model,
            )

    def finetune(self, pretrained_model_path: str) -> None:
        """Finetune model based on given pretrained model path.

        Args:
            pretrained_model_path (str): Pretrained model path or url.
        """
        # load pretrained model
        save_load.load_pretrain(self.model, pretrained_model_path, self.equation)

        # call train program
        self.train()

    @misc.run_on_eval_mode
    def eval(
        self, epoch_id: Optional[int] = None
    ) -> Tuple[float, Dict[str, Dict[str, float]]]:
        """Evaluation.

        Args:
            epoch_id (Optional[int]): Epoch id. Defaults to None.

        Returns:
            Tuple[float, Dict[str, Dict[str, float]]]: A targe metric value(float) and
                all metric(s)(dict) of evaluation, used to judge the quality of the model.
        """
        # set eval func
        self.eval_func = ppsci.solver.eval.eval_func

        result = self.eval_func(self, epoch_id, self.log_freq)
        metric_msg = ", ".join(
            [self.eval_output_info[key].avg_info for key in self.eval_output_info]
        )

        if isinstance(epoch_id, int):
            logger.info(f"[Eval][Epoch {epoch_id}][Avg] {metric_msg}")
        else:
            logger.info(f"[Eval][Avg] {metric_msg}")
        self.eval_output_info.clear()

        return result

    @misc.run_on_eval_mode
    def visualize(self, epoch_id: Optional[int] = None):
        """Visualization.

        Args:
            epoch_id (Optional[int]): Epoch id. Defaults to None.
        """
        # set visualize func
        self.visu_func = ppsci.solver.visu.visualize_func

        self.visu_func(self, epoch_id)
        if isinstance(epoch_id, int):
            logger.info(f"[Visualize][Epoch {epoch_id}] Finish visualization")
        else:
            logger.info("[Visualize] Finish visualization")

    @misc.run_on_eval_mode
    def predict(
        self,
        input_dict: Dict[str, Union[np.ndarray, paddle.Tensor]],
        expr_dict: Optional[Dict[str, Callable]] = None,
        batch_size: int = 64,
        no_grad: bool = True,
        return_numpy: bool = False,
    ) -> Dict[str, Union[paddle.Tensor, np.ndarray]]:
        """Pure prediction using model.forward(...) and expression(optional, if given).

        Args:
            input_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Input data in dict.
            expr_dict (Optional[Dict[str, Callable]]): Expression dict, which guide to
                compute equation variable with callable function. Defaults to None.
            batch_size (int, optional): Predicting by batch size. Defaults to 64.
            no_grad (bool): Whether set stop_gradient=True for entire prediction, mainly
                for memory-efficiency. Defaults to True.
            return_numpy (bool): Whether convert result from Tensor to numpy ndarray.
                Defaults to False.

        Returns:
            Dict[str, Union[paddle.Tensor, np.ndarray]]: Prediction in dict.

        Examples:
            >>> import paddle
            >>> import ppsci
            >>> model = ppsci.arch.MLP(('x', 'y'), ('u', 'v'), num_layers=None, hidden_size=[32, 8])
            >>> solver = ppsci.solver.Solver(model) # doctest: +SKIP
            >>> input_dict = {'x': paddle.rand([32, 1]),
            ...               'y': paddle.rand([32, 1])}
            >>> pred = solver.predict(input_dict) # doctest: +SKIP
            >>> for k, v in pred.items(): # doctest: +SKIP
            ...     print(k, v.shape) # doctest: +SKIP
            u [32, 1]
            v [32, 1]
        """
        num_samples = len(next(iter(input_dict.values())))
        num_pad = (self.world_size - num_samples % self.world_size) % self.world_size
        # pad with last element if `num_samples` is not divisible by `world_size`
        # ensuring every device get same number of data.
        if num_pad > 0:
            for k, v in input_dict.items():
                repeat_times = (num_pad, *(1 for _ in range(v.ndim - 1)))
                if isinstance(v, np.ndarray):
                    input_dict[k] = np.concatenate(
                        (
                            v,
                            np.tile(v[num_samples - 1 : num_samples], repeat_times),
                        ),
                    )
                elif isinstance(v, paddle.Tensor):
                    input_dict[k] = paddle.concat(
                        (
                            v,
                            paddle.tile(v[num_samples - 1 : num_samples], repeat_times),
                        ),
                    )
                else:
                    raise ValueError(f"Unsupported data type {type(v)}.")

        num_samples_pad = num_samples + num_pad
        local_num_samples_pad = num_samples_pad // self.world_size
        local_input_dict = (
            {k: v[self.rank :: self.world_size] for k, v in input_dict.items()}
            if self.world_size > 1
            else input_dict
        )
        local_batch_num = (local_num_samples_pad + (batch_size - 1)) // batch_size

        pred_dict = misc.Prettydefaultdict(list)
        with self.no_grad_context_manager(no_grad), self.no_sync_context_manager(
            self.world_size > 1, self.model
        ):
            for batch_id in range(local_batch_num):
                batch_input_dict = {}
                st = batch_id * batch_size
                ed = min(local_num_samples_pad, (batch_id + 1) * batch_size)

                # prepare batch input dict
                for key in local_input_dict:
                    if not paddle.is_tensor(local_input_dict[key]):
                        batch_input_dict[key] = paddle.to_tensor(
                            local_input_dict[key][st:ed], paddle.get_default_dtype()
                        )
                    else:
                        batch_input_dict[key] = local_input_dict[key][st:ed]
                    batch_input_dict[key].stop_gradient = no_grad

                # forward
                with self.autocast_context_manager(self.use_amp, self.amp_level):
                    batch_output_dict = self.forward_helper.visu_forward(
                        expr_dict, batch_input_dict, self.model
                    )

                # collect batch data
                for key, batch_output in batch_output_dict.items():
                    pred_dict[key].append(
                        batch_output.detach() if no_grad else batch_output
                    )

            # concatenate local predictions
            pred_dict = {key: paddle.concat(value) for key, value in pred_dict.items()}

            if self.world_size > 1:
                # gather global predictions from all devices if world_size > 1
                pred_dict = {
                    key: misc.all_gather(value) for key, value in pred_dict.items()
                }
                # rearrange predictions as the same order of input_dict according
                # to inverse permutation
                perm = np.arange(num_samples_pad, dtype="int64")
                perm = np.concatenate(
                    [perm[rank :: self.world_size] for rank in range(self.world_size)],
                    axis=0,
                )
                perm_inv = np.empty_like(perm)
                perm_inv[perm] = np.arange(num_samples_pad, dtype="int64")
                perm_inv = paddle.to_tensor(perm_inv)
                pred_dict = {key: value[perm_inv] for key, value in pred_dict.items()}
                # then discard predictions of padding data at the end if num_pad > 0
                if num_pad > 0:
                    pred_dict = {
                        key: value[:num_samples] for key, value in pred_dict.items()
                    }
                    # NOTE: Discard padding data in input_dict for consistency
                    for k in input_dict:
                        input_dict[k] = input_dict[k][:num_samples]

        # convert to numpy ndarray if specified
        if return_numpy:
            pred_dict = {
                k: (v.numpy() if paddle.is_tensor(v) else v)
                for k, v in pred_dict.items()
            }

        return pred_dict

    @misc.run_on_eval_mode
    def export(
        self,
        input_spec: List["InputSpec"],
        export_path: str,
        with_onnx: bool = False,
        skip_prune_program: bool = False,
    ):
        """
        Convert model to static graph model and export to files.

        Args:
            input_spec (List[InputSpec]): InputSpec describes the signature information
                of the model input.
            export_path (str): The path prefix to save model.
            with_onnx (bool, optional): Whether to export model into onnx after
                paddle inference models are exported. Defaults to False.
            skip_prune_program (bool, optional): Whether prune program, pruning program
                may cause unexpectable result, e.g. llm-inference. Defaults to False.
        """
        jit.enable_to_static(True)

        if self.pretrained_model_path is None:
            logger.warning(
                "'pretrained_model_path' is not given, so the weights of exported "
                "model will be random initialized."
            )

        # convert model to static graph model
        static_model = jit.to_static(
            self.model,
            input_spec=input_spec,
            full_graph=True,
        )

        # save static graph model to disk
        if len(osp.dirname(export_path)):
            os.makedirs(osp.dirname(export_path), exist_ok=True)
        try:
            jit.save(static_model, export_path, skip_prune_program=skip_prune_program)
        except Exception as e:
            raise e
        logger.message(
            f"Inference model has been exported to: {export_path}, including "
            "*.pdmodel, *.pdiparams and *.pdiparams.info files."
        )
        jit.enable_to_static(False)

        if with_onnx:
            if not importlib.util.find_spec("paddle2onnx"):
                raise ModuleNotFoundError(
                    "Please install paddle2onnx with `pip install paddle2onnx`"
                    " before exporting onnx model."
                )
            import paddle2onnx

            DEFAULT_OPSET_VERSION = 13

            paddle2onnx.export(
                model_file=export_path + ".pdmodel",
                params_file=export_path + ".pdiparams",
                save_file=export_path + ".onnx",
                opset_version=DEFAULT_OPSET_VERSION,
                enable_onnx_checker=True,
            )
            logger.message(f"ONNX model has been exported to: {export_path}.onnx")

    def autocast_context_manager(
        self, enable: bool, level: Literal["O0", "O1", "O2", "OD"] = "O1"
    ) -> contextlib.AbstractContextManager:
        """Smart autocast context manager for Auto Mix Precision.

        Args:
            enable (bool): Enable autocast.
            level (Literal["O0", "O1", "O2", "OD"]): Autocast level.

        Returns:
            contextlib.AbstractContextManager: Smart autocast context manager.
        """
        if enable:
            ctx_manager = amp.auto_cast(level=level)
        else:
            ctx_manager = (
                contextlib.nullcontext()
                if sys.version_info >= (3, 7)
                else contextlib.suppress()
            )
        return ctx_manager

    @functools.lru_cache()
    def no_grad_context_manager(
        self, enable: bool
    ) -> contextlib.AbstractContextManager:
        """Smart no_grad context manager.

        Args:
            enable (bool): Enable no_grad.

        Returns:
            contextlib.AbstractContextManager: Smart no_grad context manager.
        """
        if enable:
            ctx_manager = paddle.no_grad()
        else:
            ctx_manager = (
                contextlib.nullcontext()
                if sys.version_info >= (3, 7)
                else contextlib.suppress()
            )
        return ctx_manager

    def no_sync_context_manager(
        self,
        enable: bool,
        ddp_model: paddle.DataParallel,
    ) -> contextlib.AbstractContextManager:
        """Smart no_sync context manager for given model.
        NOTE: Only `paddle.DataParallel` object has `no_sync` interface.

        Args:
            enable (bool): Enable no_sync.

        Returns:
            contextlib.AbstractContextManager: Smart no_sync context manager.
        """
        if enable:
            if isinstance(self.model, ppsci.arch.ModelList):
                for model in self.model.model_list:
                    if not isinstance(model, paddle.DataParallel):
                        raise TypeError(
                            "no_sync interface is only for model with type "
                            "paddle.DataParallel, but got type "
                            f"{misc.typename(model)}"
                        )
                ctx_manager = contextlib.ExitStack()
                for model in self.model.model_list:
                    ctx_manager.enter_context(model.no_sync())
            else:
                if not isinstance(self.model, paddle.DataParallel):
                    raise TypeError(
                        "no_sync interface is only for model with type "
                        f"paddle.DataParallel, but got type {misc.typename(ddp_model)}"
                    )
                ctx_manager = ddp_model.no_sync()
        else:
            ctx_manager = (
                contextlib.nullcontext()
                if sys.version_info >= (3, 7)
                else contextlib.suppress()
            )
        return ctx_manager

    def plot_loss_history(
        self,
        by_epoch: bool = False,
        smooth_step: int = 1,
        use_semilogy: bool = True,
    ) -> None:
        """Plotting iteration/epoch-loss curve.

        Args:
            by_epoch (bool, optional): Whether the abscissa axis of the curve is epoch or iteration. Defaults to False.
            smooth_step (int, optional): How many steps of loss are squeezed to one point to smooth the curve. Defaults to 1.
            use_semilogy (bool, optional): Whether to set non-uniform coordinates for the y-axis. Defaults to True.
        """
        loss_dict = {}
        for key in self.train_loss_info:
            loss_arr = np.asarray(self.train_loss_info[key].history)
            if by_epoch:
                loss_arr = np.mean(
                    np.reshape(loss_arr, (-1, self.iters_per_epoch)),
                    axis=1,
                )
            loss_dict[key] = list(loss_arr)

        misc.plot_curve(
            data=loss_dict,
            xlabel="Epoch" if by_epoch else "Iteration",
            ylabel="Loss",
            output_dir=self.output_dir,
            smooth_step=smooth_step,
            use_semilogy=use_semilogy,
        )

    def _parse_params_from_cfg(self, cfg: DictConfig):
        """
        Parse hyper-parameters from DictConfig.
        """
        self.output_dir = cfg.output_dir
        self.log_freq = cfg.log_freq
        self.use_tbd = cfg.use_tbd
        self.use_vdl = cfg.use_vdl
        self.wandb_config = cfg.wandb_config
        self.use_wandb = cfg.use_wandb
        self.device = cfg.device
        self.to_static = cfg.to_static

        self.use_amp = cfg.use_amp
        self.amp_level = cfg.amp_level

        self.epochs = cfg.TRAIN.epochs
        self.iters_per_epoch = cfg.TRAIN.iters_per_epoch
        self.update_freq = cfg.TRAIN.update_freq
        self.save_freq = cfg.TRAIN.save_freq
        self.eval_during_train = cfg.TRAIN.eval_during_train
        self.start_eval_epoch = cfg.TRAIN.start_eval_epoch
        self.eval_freq = cfg.TRAIN.eval_freq
        self.checkpoint_path = cfg.TRAIN.checkpoint_path

        if "ema" in cfg.TRAIN and cfg.TRAIN.ema.get("use_ema", False):
            self.avg_freq = cfg.TRAIN.ema.avg_freq
        elif "swa" in cfg.TRAIN and cfg.TRAIN.swa.get("use_swa", False):
            self.avg_freq = cfg.TRAIN.swa.avg_freq

        self.compute_metric_by_batch = cfg.EVAL.compute_metric_by_batch
        self.eval_with_no_grad = cfg.EVAL.eval_with_no_grad

        if cfg.mode == "train":
            self.pretrained_model_path = cfg.TRAIN.pretrained_model_path
        elif cfg.mode == "eval":
            self.pretrained_model_path = cfg.EVAL.pretrained_model_path
        elif cfg.mode in ["export", "infer"]:
            self.pretrained_model_path = cfg.INFER.pretrained_model_path

autocast_context_manager(enable, level='O1')

Smart autocast context manager for Auto Mix Precision.

Parameters:

Name	Type	Description	Default
enable	bool	Enable autocast.	required
level	Literal['O0', 'O1', 'O2', 'OD']	Autocast level.	'O1'

Returns:

Type	Description
AbstractContextManager	contextlib.AbstractContextManager: Smart autocast context manager.

Source code in ppsci/solver/solver.py

def autocast_context_manager(
    self, enable: bool, level: Literal["O0", "O1", "O2", "OD"] = "O1"
) -> contextlib.AbstractContextManager:
    """Smart autocast context manager for Auto Mix Precision.

    Args:
        enable (bool): Enable autocast.
        level (Literal["O0", "O1", "O2", "OD"]): Autocast level.

    Returns:
        contextlib.AbstractContextManager: Smart autocast context manager.
    """
    if enable:
        ctx_manager = amp.auto_cast(level=level)
    else:
        ctx_manager = (
            contextlib.nullcontext()
            if sys.version_info >= (3, 7)
            else contextlib.suppress()
        )
    return ctx_manager

eval(epoch_id=None)

Evaluation.

Parameters:

Name	Type	Description	Default
epoch_id	Optional[int]	Epoch id. Defaults to None.	None

Returns:

Type	Description
Tuple[float, Dict[str, Dict[str, float]]]	Tuple[float, Dict[str, Dict[str, float]]]: A targe metric value(float) and all metric(s)(dict) of evaluation, used to judge the quality of the model.

Source code in ppsci/solver/solver.py

@misc.run_on_eval_mode
def eval(
    self, epoch_id: Optional[int] = None
) -> Tuple[float, Dict[str, Dict[str, float]]]:
    """Evaluation.

    Args:
        epoch_id (Optional[int]): Epoch id. Defaults to None.

    Returns:
        Tuple[float, Dict[str, Dict[str, float]]]: A targe metric value(float) and
            all metric(s)(dict) of evaluation, used to judge the quality of the model.
    """
    # set eval func
    self.eval_func = ppsci.solver.eval.eval_func

    result = self.eval_func(self, epoch_id, self.log_freq)
    metric_msg = ", ".join(
        [self.eval_output_info[key].avg_info for key in self.eval_output_info]
    )

    if isinstance(epoch_id, int):
        logger.info(f"[Eval][Epoch {epoch_id}][Avg] {metric_msg}")
    else:
        logger.info(f"[Eval][Avg] {metric_msg}")
    self.eval_output_info.clear()

    return result

export(input_spec, export_path, with_onnx=False, skip_prune_program=False)

Convert model to static graph model and export to files.

Parameters:

Name	Type	Description	Default
input_spec	List[InputSpec]	InputSpec describes the signature information of the model input.	required
export_path	str	The path prefix to save model.	required
with_onnx	bool	Whether to export model into onnx after paddle inference models are exported. Defaults to False.	False
skip_prune_program	bool	Whether prune program, pruning program may cause unexpectable result, e.g. llm-inference. Defaults to False.	False

Source code in ppsci/solver/solver.py

@misc.run_on_eval_mode
def export(
    self,
    input_spec: List["InputSpec"],
    export_path: str,
    with_onnx: bool = False,
    skip_prune_program: bool = False,
):
    """
    Convert model to static graph model and export to files.

    Args:
        input_spec (List[InputSpec]): InputSpec describes the signature information
            of the model input.
        export_path (str): The path prefix to save model.
        with_onnx (bool, optional): Whether to export model into onnx after
            paddle inference models are exported. Defaults to False.
        skip_prune_program (bool, optional): Whether prune program, pruning program
            may cause unexpectable result, e.g. llm-inference. Defaults to False.
    """
    jit.enable_to_static(True)

    if self.pretrained_model_path is None:
        logger.warning(
            "'pretrained_model_path' is not given, so the weights of exported "
            "model will be random initialized."
        )

    # convert model to static graph model
    static_model = jit.to_static(
        self.model,
        input_spec=input_spec,
        full_graph=True,
    )

    # save static graph model to disk
    if len(osp.dirname(export_path)):
        os.makedirs(osp.dirname(export_path), exist_ok=True)
    try:
        jit.save(static_model, export_path, skip_prune_program=skip_prune_program)
    except Exception as e:
        raise e
    logger.message(
        f"Inference model has been exported to: {export_path}, including "
        "*.pdmodel, *.pdiparams and *.pdiparams.info files."
    )
    jit.enable_to_static(False)

    if with_onnx:
        if not importlib.util.find_spec("paddle2onnx"):
            raise ModuleNotFoundError(
                "Please install paddle2onnx with `pip install paddle2onnx`"
                " before exporting onnx model."
            )
        import paddle2onnx

        DEFAULT_OPSET_VERSION = 13

        paddle2onnx.export(
            model_file=export_path + ".pdmodel",
            params_file=export_path + ".pdiparams",
            save_file=export_path + ".onnx",
            opset_version=DEFAULT_OPSET_VERSION,
            enable_onnx_checker=True,
        )
        logger.message(f"ONNX model has been exported to: {export_path}.onnx")

finetune(pretrained_model_path)

Finetune model based on given pretrained model path.

Parameters:

Name	Type	Description	Default
pretrained_model_path	str	Pretrained model path or url.	required

Source code in ppsci/solver/solver.py

def finetune(self, pretrained_model_path: str) -> None:
    """Finetune model based on given pretrained model path.

    Args:
        pretrained_model_path (str): Pretrained model path or url.
    """
    # load pretrained model
    save_load.load_pretrain(self.model, pretrained_model_path, self.equation)

    # call train program
    self.train()

no_grad_context_manager(enable) cached

Smart no_grad context manager.

Parameters:

Name	Type	Description	Default
enable	bool	Enable no_grad.	required

Returns:

Type	Description
AbstractContextManager	contextlib.AbstractContextManager: Smart no_grad context manager.

Source code in ppsci/solver/solver.py

@functools.lru_cache()
def no_grad_context_manager(
    self, enable: bool
) -> contextlib.AbstractContextManager:
    """Smart no_grad context manager.

    Args:
        enable (bool): Enable no_grad.

    Returns:
        contextlib.AbstractContextManager: Smart no_grad context manager.
    """
    if enable:
        ctx_manager = paddle.no_grad()
    else:
        ctx_manager = (
            contextlib.nullcontext()
            if sys.version_info >= (3, 7)
            else contextlib.suppress()
        )
    return ctx_manager

no_sync_context_manager(enable, ddp_model)

Smart no_sync context manager for given model. NOTE: Only paddle.DataParallel object has no_sync interface.

Parameters:

Name	Type	Description	Default
enable	bool	Enable no_sync.	required

Returns:

Type	Description
AbstractContextManager	contextlib.AbstractContextManager: Smart no_sync context manager.

Source code in ppsci/solver/solver.py

def no_sync_context_manager(
    self,
    enable: bool,
    ddp_model: paddle.DataParallel,
) -> contextlib.AbstractContextManager:
    """Smart no_sync context manager for given model.
    NOTE: Only `paddle.DataParallel` object has `no_sync` interface.

    Args:
        enable (bool): Enable no_sync.

    Returns:
        contextlib.AbstractContextManager: Smart no_sync context manager.
    """
    if enable:
        if isinstance(self.model, ppsci.arch.ModelList):
            for model in self.model.model_list:
                if not isinstance(model, paddle.DataParallel):
                    raise TypeError(
                        "no_sync interface is only for model with type "
                        "paddle.DataParallel, but got type "
                        f"{misc.typename(model)}"
                    )
            ctx_manager = contextlib.ExitStack()
            for model in self.model.model_list:
                ctx_manager.enter_context(model.no_sync())
        else:
            if not isinstance(self.model, paddle.DataParallel):
                raise TypeError(
                    "no_sync interface is only for model with type "
                    f"paddle.DataParallel, but got type {misc.typename(ddp_model)}"
                )
            ctx_manager = ddp_model.no_sync()
    else:
        ctx_manager = (
            contextlib.nullcontext()
            if sys.version_info >= (3, 7)
            else contextlib.suppress()
        )
    return ctx_manager

plot_loss_history(by_epoch=False, smooth_step=1, use_semilogy=True)

Plotting iteration/epoch-loss curve.

Parameters:

Name	Type	Description	Default
by_epoch	bool	Whether the abscissa axis of the curve is epoch or iteration. Defaults to False.	False
smooth_step	int	How many steps of loss are squeezed to one point to smooth the curve. Defaults to 1.	1
use_semilogy	bool	Whether to set non-uniform coordinates for the y-axis. Defaults to True.	True

Source code in ppsci/solver/solver.py

def plot_loss_history(
    self,
    by_epoch: bool = False,
    smooth_step: int = 1,
    use_semilogy: bool = True,
) -> None:
    """Plotting iteration/epoch-loss curve.

    Args:
        by_epoch (bool, optional): Whether the abscissa axis of the curve is epoch or iteration. Defaults to False.
        smooth_step (int, optional): How many steps of loss are squeezed to one point to smooth the curve. Defaults to 1.
        use_semilogy (bool, optional): Whether to set non-uniform coordinates for the y-axis. Defaults to True.
    """
    loss_dict = {}
    for key in self.train_loss_info:
        loss_arr = np.asarray(self.train_loss_info[key].history)
        if by_epoch:
            loss_arr = np.mean(
                np.reshape(loss_arr, (-1, self.iters_per_epoch)),
                axis=1,
            )
        loss_dict[key] = list(loss_arr)

    misc.plot_curve(
        data=loss_dict,
        xlabel="Epoch" if by_epoch else "Iteration",
        ylabel="Loss",
        output_dir=self.output_dir,
        smooth_step=smooth_step,
        use_semilogy=use_semilogy,
    )

predict(input_dict, expr_dict=None, batch_size=64, no_grad=True, return_numpy=False)

Pure prediction using model.forward(...) and expression(optional, if given).

Parameters:

Name	Type	Description	Default
input_dict	Dict[str, Union[ndarray, Tensor]]	Input data in dict.	required
expr_dict	Optional[Dict[str, Callable]]	Expression dict, which guide to compute equation variable with callable function. Defaults to None.	None
batch_size	int	Predicting by batch size. Defaults to 64.	64
no_grad	bool	Whether set stop_gradient=True for entire prediction, mainly for memory-efficiency. Defaults to True.	True
return_numpy	bool	Whether convert result from Tensor to numpy ndarray. Defaults to False.	False

Returns:

Type	Description
Dict[str, Union[Tensor, ndarray]]	Dict[str, Union[paddle.Tensor, np.ndarray]]: Prediction in dict.

Examples:

>>> import paddle
>>> import ppsci
>>> model = ppsci.arch.MLP(('x', 'y'), ('u', 'v'), num_layers=None, hidden_size=[32, 8])
>>> solver = ppsci.solver.Solver(model)
>>> input_dict = {'x': paddle.rand([32, 1]),
...               'y': paddle.rand([32, 1])}
>>> pred = solver.predict(input_dict)
>>> for k, v in pred.items():
...     print(k, v.shape)
u [32, 1]
v [32, 1]

Source code in ppsci/solver/solver.py

@misc.run_on_eval_mode
def predict(
    self,
    input_dict: Dict[str, Union[np.ndarray, paddle.Tensor]],
    expr_dict: Optional[Dict[str, Callable]] = None,
    batch_size: int = 64,
    no_grad: bool = True,
    return_numpy: bool = False,
) -> Dict[str, Union[paddle.Tensor, np.ndarray]]:
    """Pure prediction using model.forward(...) and expression(optional, if given).

    Args:
        input_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Input data in dict.
        expr_dict (Optional[Dict[str, Callable]]): Expression dict, which guide to
            compute equation variable with callable function. Defaults to None.
        batch_size (int, optional): Predicting by batch size. Defaults to 64.
        no_grad (bool): Whether set stop_gradient=True for entire prediction, mainly
            for memory-efficiency. Defaults to True.
        return_numpy (bool): Whether convert result from Tensor to numpy ndarray.
            Defaults to False.

    Returns:
        Dict[str, Union[paddle.Tensor, np.ndarray]]: Prediction in dict.

    Examples:
        >>> import paddle
        >>> import ppsci
        >>> model = ppsci.arch.MLP(('x', 'y'), ('u', 'v'), num_layers=None, hidden_size=[32, 8])
        >>> solver = ppsci.solver.Solver(model) # doctest: +SKIP
        >>> input_dict = {'x': paddle.rand([32, 1]),
        ...               'y': paddle.rand([32, 1])}
        >>> pred = solver.predict(input_dict) # doctest: +SKIP
        >>> for k, v in pred.items(): # doctest: +SKIP
        ...     print(k, v.shape) # doctest: +SKIP
        u [32, 1]
        v [32, 1]
    """
    num_samples = len(next(iter(input_dict.values())))
    num_pad = (self.world_size - num_samples % self.world_size) % self.world_size
    # pad with last element if `num_samples` is not divisible by `world_size`
    # ensuring every device get same number of data.
    if num_pad > 0:
        for k, v in input_dict.items():
            repeat_times = (num_pad, *(1 for _ in range(v.ndim - 1)))
            if isinstance(v, np.ndarray):
                input_dict[k] = np.concatenate(
                    (
                        v,
                        np.tile(v[num_samples - 1 : num_samples], repeat_times),
                    ),
                )
            elif isinstance(v, paddle.Tensor):
                input_dict[k] = paddle.concat(
                    (
                        v,
                        paddle.tile(v[num_samples - 1 : num_samples], repeat_times),
                    ),
                )
            else:
                raise ValueError(f"Unsupported data type {type(v)}.")

    num_samples_pad = num_samples + num_pad
    local_num_samples_pad = num_samples_pad // self.world_size
    local_input_dict = (
        {k: v[self.rank :: self.world_size] for k, v in input_dict.items()}
        if self.world_size > 1
        else input_dict
    )
    local_batch_num = (local_num_samples_pad + (batch_size - 1)) // batch_size

    pred_dict = misc.Prettydefaultdict(list)
    with self.no_grad_context_manager(no_grad), self.no_sync_context_manager(
        self.world_size > 1, self.model
    ):
        for batch_id in range(local_batch_num):
            batch_input_dict = {}
            st = batch_id * batch_size
            ed = min(local_num_samples_pad, (batch_id + 1) * batch_size)

            # prepare batch input dict
            for key in local_input_dict:
                if not paddle.is_tensor(local_input_dict[key]):
                    batch_input_dict[key] = paddle.to_tensor(
                        local_input_dict[key][st:ed], paddle.get_default_dtype()
                    )
                else:
                    batch_input_dict[key] = local_input_dict[key][st:ed]
                batch_input_dict[key].stop_gradient = no_grad

            # forward
            with self.autocast_context_manager(self.use_amp, self.amp_level):
                batch_output_dict = self.forward_helper.visu_forward(
                    expr_dict, batch_input_dict, self.model
                )

            # collect batch data
            for key, batch_output in batch_output_dict.items():
                pred_dict[key].append(
                    batch_output.detach() if no_grad else batch_output
                )

        # concatenate local predictions
        pred_dict = {key: paddle.concat(value) for key, value in pred_dict.items()}

        if self.world_size > 1:
            # gather global predictions from all devices if world_size > 1
            pred_dict = {
                key: misc.all_gather(value) for key, value in pred_dict.items()
            }
            # rearrange predictions as the same order of input_dict according
            # to inverse permutation
            perm = np.arange(num_samples_pad, dtype="int64")
            perm = np.concatenate(
                [perm[rank :: self.world_size] for rank in range(self.world_size)],
                axis=0,
            )
            perm_inv = np.empty_like(perm)
            perm_inv[perm] = np.arange(num_samples_pad, dtype="int64")
            perm_inv = paddle.to_tensor(perm_inv)
            pred_dict = {key: value[perm_inv] for key, value in pred_dict.items()}
            # then discard predictions of padding data at the end if num_pad > 0
            if num_pad > 0:
                pred_dict = {
                    key: value[:num_samples] for key, value in pred_dict.items()
                }
                # NOTE: Discard padding data in input_dict for consistency
                for k in input_dict:
                    input_dict[k] = input_dict[k][:num_samples]

    # convert to numpy ndarray if specified
    if return_numpy:
        pred_dict = {
            k: (v.numpy() if paddle.is_tensor(v) else v)
            for k, v in pred_dict.items()
        }

    return pred_dict

train()

Training.

Source code in ppsci/solver/solver.py

def train(self) -> None:
    """Training."""
    self.global_step = self.best_metric["epoch"] * self.iters_per_epoch
    start_epoch = self.best_metric["epoch"] + 1

    if self.nvtx_flag:
        core.nvprof_start()
        core.nvprof_enable_record_event()

    for epoch_id in range(start_epoch, self.epochs + 1):
        self.train_epoch_func(self, epoch_id, self.log_freq)
        self.train_output_info.clear()

        # update average model if exist
        if self.ema_model and epoch_id % self.avg_freq == 0:
            self.ema_model.update()

        cur_metric = float("inf")
        # evaluate during training
        if (
            self.eval_during_train
            and epoch_id % self.eval_freq == 0
            and epoch_id >= self.start_eval_epoch
        ):
            cur_metric, metric_dict_group = self.eval(epoch_id)
            if cur_metric < self.best_metric["metric"]:
                self.best_metric["metric"] = cur_metric
                self.best_metric["epoch"] = epoch_id
                save_load.save_checkpoint(
                    self.model,
                    self.optimizer,
                    self.best_metric,
                    self.scaler,
                    self.output_dir,
                    "best_model",
                    self.equation,
                )
            logger.info(
                f"[Eval][Epoch {epoch_id}]"
                f"[best metric: {self.best_metric['metric']}]"
            )
            for metric_name, metric_dict in metric_dict_group.items():
                logger.scalar(
                    {f"eval/{metric_name}/{k}": v for k, v in metric_dict.items()},
                    epoch_id,
                    self.vdl_writer,
                    self.wandb_writer,
                    self.tbd_writer,
                )

            # visualize after evaluation
            if self.visualizer is not None:
                self.visualize(epoch_id)

            # evaluation for moving average evaluation(almost same procedure)
            if self.ema_model and epoch_id % self.avg_freq == 0:
                self.ema_model.apply_shadow()
                logger.info("Evaluating metric of averaging model...")
                cur_metric_ema, metric_dict_group_ema = self.eval(epoch_id)
                self.ema_model.restore()

                if cur_metric_ema < self.best_metric["metric"]:
                    self.best_metric["metric"] = cur_metric_ema
                    self.best_metric["epoch"] = epoch_id
                    save_load.save_checkpoint(
                        self.ema_model,
                        None,
                        metric=self.best_metric,
                        output_dir=self.output_dir,
                        prefix="best_model_ema",
                    )
                logger.info(
                    f"[Eval][Epoch {epoch_id}]"
                    f"[best metric: {self.best_metric['metric']}]"
                )
                for metric_name, metric_dict in metric_dict_group_ema.items():
                    logger.scalar(
                        {
                            f"eval_ema/{metric_name}/{k}": v
                            for k, v in metric_dict.items()
                        },
                        epoch_id,
                        self.vdl_writer,
                        self.wandb_writer,
                        self.tbd_writer,
                    )

        # update learning rate by epoch
        if self.lr_scheduler is not None and self.lr_scheduler.by_epoch:
            self.lr_scheduler.step()

        # save epoch model every save_freq epochs
        if self.save_freq > 0 and epoch_id % self.save_freq == 0:
            save_load.save_checkpoint(
                self.model,
                self.optimizer,
                {"metric": cur_metric, "epoch": epoch_id},
                self.scaler,
                self.output_dir,
                f"epoch_{epoch_id}",
                self.equation,
                ema_model=self.ema_model,
            )

        # save the latest model for convenient resume training
        save_load.save_checkpoint(
            self.model,
            self.optimizer,
            {"metric": cur_metric, "epoch": epoch_id},
            self.scaler,
            self.output_dir,
            "latest",
            self.equation,
            print_log=(epoch_id == start_epoch),
            ema_model=self.ema_model,
        )

visualize(epoch_id=None)

Visualization.

Parameters:

Name	Type	Description	Default
epoch_id	Optional[int]	Epoch id. Defaults to None.	None

Source code in ppsci/solver/solver.py

@misc.run_on_eval_mode
def visualize(self, epoch_id: Optional[int] = None):
    """Visualization.

    Args:
        epoch_id (Optional[int]): Epoch id. Defaults to None.
    """
    # set visualize func
    self.visu_func = ppsci.solver.visu.visualize_func

    self.visu_func(self, epoch_id)
    if isinstance(epoch_id, int):
        logger.info(f"[Visualize][Epoch {epoch_id}] Finish visualization")
    else:
        logger.info("[Visualize] Finish visualization")

ppsci.solver.train

train_epoch_func(solver, epoch_id, log_freq)

Train program for one epoch.

Parameters:

Name	Type	Description	Default
solver	Solver	Main solver.	required
epoch_id	int	Epoch id.	required
log_freq	int	Log training information every log_freq steps.	required

Source code in ppsci/solver/train.py

def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
    """Train program for one epoch.

    Args:
        solver (solver.Solver): Main solver.
        epoch_id (int): Epoch id.
        log_freq (int): Log training information every `log_freq` steps.
    """
    batch_tic = time.perf_counter()

    for iter_id in range(1, solver.iters_per_epoch + 1):
        if solver.nvtx_flag:  # only for nsight analysis
            core.nvprof_nvtx_push(
                f"Training iteration {solver.global_step + 1}"
            )  # Training iteration

        total_batch_size = 0
        reader_cost = 0.0
        batch_cost = 0.0
        reader_tic = time.perf_counter()

        input_dicts = []
        label_dicts = []
        weight_dicts = []
        for _, _constraint in solver.constraint.items():
            # fetch data from data loader
            try:
                input_dict, label_dict, weight_dict = next(_constraint.data_iter)
            except StopIteration:
                _constraint.data_iter = iter(_constraint.data_loader)
                input_dict, label_dict, weight_dict = next(_constraint.data_iter)
            reader_cost += time.perf_counter() - reader_tic

            # NOTE: eliminate first 5 step for warmup
            if iter_id == 5:
                for key in solver.train_time_info:
                    solver.train_time_info[key].reset()

            for v in input_dict.values():
                if hasattr(v, "stop_gradient"):
                    v.stop_gradient = False

            # gather each constraint's input, label, weight to a list
            input_dicts.append(input_dict)
            label_dicts.append(label_dict)
            weight_dicts.append(weight_dict)
            total_batch_size += next(iter(input_dict.values())).shape[0]
            reader_tic = time.perf_counter()

        loss_dict = misc.Prettydefaultdict(float)
        loss_dict["loss"] = 0.0
        # forward for every constraint, including model and equation expression
        with solver.no_sync_context_manager(solver.world_size > 1, solver.model):
            with solver.autocast_context_manager(solver.use_amp, solver.amp_level):
                if solver.nvtx_flag:  # only for nsight analysis
                    core.nvprof_nvtx_push("Loss computation")

                constraint_losses = solver.forward_helper.train_forward(
                    tuple(
                        _constraint.output_expr
                        for _constraint in solver.constraint.values()
                    ),
                    input_dicts,
                    solver.model,
                    solver.constraint,
                    label_dicts,
                    weight_dicts,
                )

                if solver.nvtx_flag:  # only for nsight analysis
                    core.nvprof_nvtx_pop()  # Loss computation

                # accumulate all losses
                if solver.nvtx_flag:  # only for nsight analysis
                    core.nvprof_nvtx_push("Loss aggregator")

                total_loss = solver.loss_aggregator(
                    constraint_losses, solver.global_step
                )
                if solver.update_freq > 1:
                    total_loss = total_loss / solver.update_freq

                for i, _constraint in enumerate(solver.constraint.values()):
                    loss_dict[_constraint.name] = (
                        float(constraint_losses[i]) / solver.update_freq
                    )
                loss_dict["loss"] = float(total_loss)

                if solver.nvtx_flag:  # only for nsight analysis
                    core.nvprof_nvtx_pop()  # Loss aggregator

            # backward
            if solver.nvtx_flag:  # only for nsight analysis
                core.nvprof_nvtx_push("Loss backward")

            if solver.use_amp:
                total_loss_scaled = solver.scaler.scale(total_loss)
                total_loss_scaled.backward()
            else:
                total_loss.backward()

            if solver.nvtx_flag:  # only for nsight analysis
                core.nvprof_nvtx_pop()  # Loss backward

        # update parameters
        if iter_id % solver.update_freq == 0 or iter_id == solver.iters_per_epoch:
            if solver.nvtx_flag:  # only for nsight analysis
                core.nvprof_nvtx_push("Optimizer update")

            if solver.world_size > 1:
                # fuse + allreduce manually before optimization if use DDP + no_sync
                # details in https://github.com/PaddlePaddle/Paddle/issues/48898#issuecomment-1343838622
                hpu.fused_allreduce_gradients(list(solver.model.parameters()), None)
            if solver.use_amp:
                solver.scaler.minimize(solver.optimizer, total_loss_scaled)
            else:
                solver.optimizer.step()

            if solver.nvtx_flag:  # only for nsight analysis
                core.nvprof_nvtx_pop()  # Optimizer update

            solver.optimizer.clear_grad()

        # update learning rate by step
        if solver.lr_scheduler is not None and not solver.lr_scheduler.by_epoch:
            solver.lr_scheduler.step()

        if solver.benchmark_flag:
            paddle.device.synchronize()
        batch_cost += time.perf_counter() - batch_tic

        # update and log training information
        solver.global_step += 1
        solver.train_time_info["reader_cost"].update(reader_cost)
        solver.train_time_info["batch_cost"].update(batch_cost)
        printer.update_train_loss(solver, loss_dict, total_batch_size)
        if solver.global_step % log_freq == 0 or solver.global_step == 1:
            printer.log_train_info(solver, total_batch_size, epoch_id, iter_id)

        batch_tic = time.perf_counter()

        if solver.nvtx_flag:  # only for nsight analysis
            core.nvprof_nvtx_pop()  # Training iteration
            NVTX_STOP_ITER = 25
            if solver.global_step >= NVTX_STOP_ITER:
                print(
                    f"Only run {NVTX_STOP_ITER} steps when 'NVTX' is set in environment"
                    " for nsight analysis. Exit now ......\n"
                )
                core.nvprof_stop()
                sys.exit(0)

train_LBFGS_epoch_func(solver, epoch_id, log_freq)

Train function for one epoch with L-BFGS optimizer.

NOTE: L-BFGS training program do not support AMP now.

Parameters:

Name	Type	Description	Default
solver	Solver	Main solver.	required
epoch_id	int	Epoch id.	required
log_freq	int	Log training information every log_freq steps.	required

Source code in ppsci/solver/train.py

def train_LBFGS_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
    """Train function for one epoch with L-BFGS optimizer.

    NOTE: L-BFGS training program do not support AMP now.

    Args:
        solver (solver.Solver): Main solver.
        epoch_id (int): Epoch id.
        log_freq (int): Log training information every `log_freq` steps.
    """
    batch_tic = time.perf_counter()

    for iter_id in range(1, solver.iters_per_epoch + 1):
        loss_dict = misc.Prettydefaultdict(float)
        loss_dict["loss"] = 0.0
        total_batch_size = 0
        reader_cost = 0.0
        batch_cost = 0.0
        reader_tic = time.perf_counter()

        input_dicts = []
        label_dicts = []
        weight_dicts = []
        for _, _constraint in solver.constraint.items():
            # fetch data from data loader
            try:
                input_dict, label_dict, weight_dict = next(_constraint.data_iter)
            except StopIteration:
                _constraint.data_iter = iter(_constraint.data_loader)
                input_dict, label_dict, weight_dict = next(_constraint.data_iter)
            reader_cost += time.perf_counter() - reader_tic

            for v in input_dict.values():
                if hasattr(v, "stop_gradient"):
                    v.stop_gradient = False

            # gather each constraint's input, label, weight to a list
            input_dicts.append(input_dict)
            label_dicts.append(label_dict)
            weight_dicts.append(weight_dict)
            total_batch_size += next(iter(input_dict.values())).shape[0]
            reader_tic = time.perf_counter()

        def closure() -> paddle.Tensor:
            """Forward-backward closure function for LBFGS optimizer.

            Returns:
                paddle.Tensor: Computed loss scalar.
            """
            with solver.no_sync_context_manager(solver.world_size > 1, solver.model):
                with solver.autocast_context_manager(solver.use_amp, solver.amp_level):
                    # forward for every constraint, including model and equation expression
                    constraint_losses = solver.forward_helper.train_forward(
                        tuple(
                            _constraint.output_expr
                            for _constraint in solver.constraint.values()
                        ),
                        input_dicts,
                        solver.model,
                        solver.constraint,
                        label_dicts,
                        weight_dicts,
                    )

                    total_loss = solver.loss_aggregator(
                        constraint_losses, solver.global_step
                    )
                    # accumulate all losses
                    for i, _constraint in enumerate(solver.constraint.values()):
                        loss_dict[_constraint.name] = float(constraint_losses[i])
                    loss_dict["loss"] = float(total_loss)

                # backward
                solver.optimizer.clear_grad()
                total_loss.backward()

            if solver.world_size > 1:
                # fuse + allreduce manually before optimization if use DDP model
                # details in https://github.com/PaddlePaddle/Paddle/issues/48898#issuecomment-1343838622
                hpu.fused_allreduce_gradients(list(solver.model.parameters()), None)

            return total_loss

        # update parameters
        solver.optimizer.step(closure)

        # update learning rate by step
        if solver.lr_scheduler is not None and not solver.lr_scheduler.by_epoch:
            solver.lr_scheduler.step()

        if solver.benchmark_flag:
            paddle.device.synchronize()
        batch_cost += time.perf_counter() - batch_tic

        # update and log training information
        solver.global_step += 1
        solver.train_time_info["reader_cost"].update(reader_cost)
        solver.train_time_info["batch_cost"].update(batch_cost)
        printer.update_train_loss(solver, loss_dict, total_batch_size)
        if solver.global_step % log_freq == 0 or solver.global_step == 1:
            printer.log_train_info(solver, total_batch_size, epoch_id, iter_id)

        batch_tic = time.perf_counter()

ppsci.solver.eval

eval_func(solver, epoch_id, log_freq)

Evaluation function.

Parameters:

Name	Type	Description	Default
solver	Solver	Main Solver.	required
epoch_id	Optional[int]	Epoch id.	required
log_freq	int	Log evaluation information every log_freq steps.	required

Returns:

Type	Description
Tuple[float, Dict[str, Dict[str, float]]]	Tuple[float, Dict[str, Dict[str, float]]]: Target metric and all metric dicts computed during evaluation.

Source code in ppsci/solver/eval.py

def eval_func(
    solver: "solver.Solver", epoch_id: Optional[int], log_freq: int
) -> Tuple[float, Dict[str, Dict[str, float]]]:
    """Evaluation function.

    Args:
        solver (solver.Solver): Main Solver.
        epoch_id (Optional[int]): Epoch id.
        log_freq (int): Log evaluation information every `log_freq` steps.

    Returns:
        Tuple[float, Dict[str, Dict[str, float]]]: Target metric and all metric dicts
            computed during evaluation.
    """
    if solver.compute_metric_by_batch:
        return _eval_by_batch(solver, epoch_id, log_freq)
    return _eval_by_dataset(solver, epoch_id, log_freq)

ppsci.solver.visu

visualize_func(solver, epoch_id)

Visualization program.

Parameters:

Name	Type	Description	Default
solver	Solver	Main Solver.	required
epoch_id	Optional[int]	Epoch id.	required

Source code in ppsci/solver/visu.py

def visualize_func(solver: "solver.Solver", epoch_id: Optional[int]):
    """Visualization program.

    Args:
        solver (solver.Solver): Main Solver.
        epoch_id (Optional[int]): Epoch id.
    """
    for _, _visualizer in solver.visualizer.items():
        all_input = misc.Prettydefaultdict(list)
        all_output = misc.Prettydefaultdict(list)

        # NOTE: 'visualize_func' now do not apply data sharding(different from 'Solver.predict'),
        # where every rank receive same input data and compute same output data
        # (which will cause computational redundancy),
        # but only the 0-rank(master) device save the visualization result into disk.
        # TODO(HydrogenSulfate): This will be optimized in the future.

        input_dict = _visualizer.input_dict
        batch_size = _visualizer.batch_size
        num_samples = len(next(iter(input_dict.values())))
        batch_num = (num_samples + (batch_size - 1)) // batch_size

        for batch_id in range(batch_num):
            batch_input_dict = {}
            st = batch_id * batch_size
            ed = min(num_samples, (batch_id + 1) * batch_size)

            # prepare batch input dict
            for key in input_dict:
                if not paddle.is_tensor(input_dict[key]):
                    batch_input_dict[key] = paddle.to_tensor(
                        input_dict[key][st:ed], paddle.get_default_dtype()
                    )
                else:
                    batch_input_dict[key] = input_dict[key][st:ed]
                batch_input_dict[key].stop_gradient = False

            # forward
            with solver.autocast_context_manager(
                solver.use_amp, solver.amp_level
            ), solver.no_grad_context_manager(solver.eval_with_no_grad):
                batch_output_dict = solver.forward_helper.visu_forward(
                    _visualizer.output_expr, batch_input_dict, solver.model
                )

            # collect batch data with dtype fixed to float32 regardless of the dtypes of
            # paddle runtime, which is most compatible with almost visualization tools.
            for key, batch_input in batch_input_dict.items():
                all_input[key].append(batch_input.detach().astype("float32"))
            for key, batch_output in batch_output_dict.items():
                all_output[key].append(batch_output.detach().astype("float32"))

        # concatenate all data
        for key in all_input:
            all_input[key] = paddle.concat(all_input[key])
        for key in all_output:
            all_output[key] = paddle.concat(all_output[key])

        # save visualization
        with misc.RankZeroOnly(solver.rank) as is_master:
            if is_master:
                visual_dir = osp.join(solver.output_dir, "visual")
                if epoch_id:
                    visual_dir = osp.join(visual_dir, f"epoch_{epoch_id}")
                os.makedirs(visual_dir, exist_ok=True)
                _visualizer.save(
                    osp.join(visual_dir, _visualizer.prefix),
                    {**all_input, **all_output},
                )

ppsci.solver.printer

update_train_loss(solver, loss_dict, batch_size)

Source code in ppsci/solver/printer.py

def update_train_loss(
    solver: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
):
    for key in loss_dict:
        if key not in solver.train_output_info:
            solver.train_output_info[key] = misc.AverageMeter(key, "7.5f")
        solver.train_output_info[key].update(float(loss_dict[key]), batch_size)
        if key not in solver.train_loss_info:
            solver.train_loss_info[key] = misc.AverageMeter(key, ".5f")
        solver.train_loss_info[key].update(float(loss_dict[key]))

update_eval_loss(solver, loss_dict, batch_size)

Source code in ppsci/solver/printer.py

def update_eval_loss(
    solver: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
):
    for key in loss_dict:
        if key not in solver.eval_output_info:
            solver.eval_output_info[key] = misc.AverageMeter(key, "7.5f")
        solver.eval_output_info[key].update(float(loss_dict[key]), batch_size)

log_train_info(solver, batch_size, epoch_id, iter_id)

Source code in ppsci/solver/printer.py

def log_train_info(
    solver: "solver.Solver", batch_size: int, epoch_id: int, iter_id: int
):
    lr_msg = f"lr: {solver.optimizer.get_lr():.5f}"

    metric_msg = ", ".join(
        [
            f"{key}: {solver.train_output_info[key].avg:.5f}"
            for key in solver.train_output_info
        ]
    )

    time_msg = ", ".join(
        [solver.train_time_info[key].mean for key in solver.train_time_info]
    )

    ips_msg = f"ips: {batch_size / solver.train_time_info['batch_cost'].avg:.2f}"
    if solver.benchmark_flag:
        ips_msg += " samples/s"

    eta_sec = (
        (solver.epochs - epoch_id + 1) * solver.iters_per_epoch - iter_id
    ) * solver.train_time_info["batch_cost"].avg
    eta_msg = f"eta: {str(datetime.timedelta(seconds=int(eta_sec)))}"

    epoch_width = len(str(solver.epochs))
    iters_width = len(str(solver.iters_per_epoch))
    log_str = (
        f"[Train][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
        f"[Iter {iter_id:>{iters_width}}/{solver.iters_per_epoch}] {lr_msg}, "
        f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
    )
    if solver.benchmark_flag:
        max_mem_reserved_msg = (
            f"max_mem_reserved: {device.cuda.max_memory_reserved() // (1 << 20)} MB"
        )
        max_mem_allocated_msg = (
            f"max_mem_allocated: {device.cuda.max_memory_allocated() // (1 << 20)} MB"
        )
        log_str += f", {max_mem_reserved_msg}, {max_mem_allocated_msg}"
    logger.info(log_str)

    logger.scalar(
        {
            "train/lr": solver.optimizer.get_lr(),
            **{
                f"train/{key}": solver.train_output_info[key].avg
                for key in solver.train_output_info
            },
        },
        step=solver.global_step,
        vdl_writer=solver.vdl_writer,
        wandb_writer=solver.wandb_writer,
        tbd_writer=solver.tbd_writer,
    )

log_eval_info(solver, batch_size, epoch_id, iters_per_epoch, iter_id)

Source code in ppsci/solver/printer.py

def log_eval_info(
    solver: "solver.Solver",
    batch_size: int,
    epoch_id: int,
    iters_per_epoch: int,
    iter_id: int,
):
    metric_msg = ", ".join(
        [
            f"{key}: {solver.eval_output_info[key].avg:.5f}"
            for key in solver.eval_output_info
        ]
    )

    time_msg = ", ".join(
        [solver.eval_time_info[key].mean for key in solver.eval_time_info]
    )

    ips_msg = f"ips: {batch_size / solver.eval_time_info['batch_cost'].avg:.2f}"

    eta_sec = (iters_per_epoch - iter_id) * solver.eval_time_info["batch_cost"].avg
    eta_msg = f"eta: {str(datetime.timedelta(seconds=int(eta_sec)))}"

    epoch_width = len(str(solver.epochs))
    iters_width = len(str(iters_per_epoch))
    if isinstance(epoch_id, int):
        logger.info(
            f"[Eval][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
            f"[Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
            f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
        )
    else:
        logger.info(
            f"[Eval][Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
            f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
        )